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Property market models have the overriding aim of predicting reasonable estimates of key dependent variables (demand, supply, rent, yield, vacancy and net absorption rate). These can be based on independent drivers of core property and economic activities. Accurate predictions can only be conducted when ample quantitative data are available with fewer uncertainties. However, a broad-fronted social, technical and ecological evolution can throw up sudden, unexpected shocks that result in the econometric outputs sceptical to unknown risk factors. Therefore, the purpose of this paper is to evaluate Australian office market forecast accuracy and to determine whether the forecasts capture extreme downside risk events.

This study follows a quantitative research approach, using secondary data analysis to test the accuracy of economists’ forecasts. The forecast accuracy evaluation encompasses the measurement of economic and property forecasts under the following phases: testing for the forecast accuracy; analysing outliers of forecast errors; and testing of causal relationships. Forecast accuracy measurement incorporates scale independent metrics that include Theil’s U values (U1 and U2) and mean absolute scaled error. Inter-quartile range rule is used for the outlier analysis. To find the causal relationships among variables, the time series regression methodology is utilised, including multiple regression analysis and Granger causality developed under the vector auto regression (VAR).

The credibility of economic and property forecasts was questionable around the period of the Global Financial Crisis (GFC); a significant man-made Black Swan event. The forecast accuracy measurement highlighted rental movement and net absorption forecast errors as the critical inaccurate predictions. These key property variables are explained by historic information and independent economic variables. However, these do not explain the changes when error time series of the variables were concerned. According to VAR estimates, all property variables have a significant causality derived from the lagged values of Australian S&P/ASX 200 (ASX) forecast errors. Therefore, lagged ASX forecast errors could be used as a warning signal to adjust property forecasts.

Secondary data were obtained from the premier Australian property markets: Canberra, Sydney, Brisbane, Adelaide, Melbourne and Perth. A limited ten-year timeframe (2001-2011) was used in the ex-post analysis for the comparison of economic and property variables. Forecasts ceased from 2011, due to the discontinuity of the Australian Financial Review quarterly survey of economists; the main source of economic forecast data.

The research strongly recommended naïve forecasts for the property variables, as an input determinant in each office market forecast equation. Further, lagged forecast errors in the ASX could be used as a warning signal for the successive property forecast errors. Hence, data adjustments can be made to ensure the accuracy of the Australian office market forecasts.

The paper highlights the critical inaccuracy of the Australian office market forecasts around the GFC. In an environment of increasing incidence of unknown events, these types of risk events should not be dismissed as statistical outliers in real estate modelling. As a proactive strategy to improve office market forecasts, lagged ASX forecast errors could be used as a warning signal. This causality was mirrored in rental movements and total vacancy forecast errors. The close interdependency between rents and vacancy rates in the forecasting process and the volatility in rental cash flows reflects on direct property investment and subsequently on the ASX, is therefore justified.

The purpose of this paper is to attest whether generalized trust variable is the best proxy for social capital in explaining the latter’s effect on economic growth in a panel setting. Via a specially formulated theoretical framework, the authors also test whether the growth-effect of social capital is direct or indirect, and if it is indirect, can property rights be the link between social capital and growth.

The authors begin with testing the robustness of generalized trust variable in explaining the effect of social capital on growth and property rights. The authors then propose a number of trust-alternative variables that are shown to contain an element of trust based on theoretical arguments drawn from previous studies, to proxy for social capital and re-estimate its effect on growth and property rights. In this study, the authors use panel estimation technique, hitherto has been limited in social capital studies, which are capable of reducing omitted variable bias and time-invariant heterogeneity compared to the commonly used cross-sectional estimation.

First, the authors find that generalized trust data obtained by the World Value Survey (WVS) are unable to yield sufficiently robust results in panel estimation due to missing observations problem. Using the proposed trust-alternative variables, the estimation results improve significantly and the authors are able to show that social capital is a deep determinant of growth and it is affecting growth via property rights channel. The findings also give supporting evidence to the primacy of informal rules and constraints as proposed by North (2005) over the political prominence theory by Acemoglu et al. (2005).

Generalized trust data obtained from the WVS, frequently used in majority of social capital studies to measure social capital, yield highly non-robust results in panel estimation due to missing observations problem. Future studies in social capital intending to use panel estimation therefore need to find trust-alternative variables to proxy for social capital, and this paper has proposed four such variables.

The use of panel estimation technique extends the evidence of social capital significance to economic growth and property rights, since the previous social capital studies rely heavily on cross-sectional estimation technique. Due to the availability of annual observations of the trust-alternative variables, this paper is able to find better results as compared to estimation using generalized trust data.

The purpose of this paper is to determine the effect that ownership and management structures have on ability to control operating expenses. For individual investors, intensity of management experience is also explored as a possible explanatory variable for operating expenses. For property management services that are contracted out, the level of the fee is investigated as a possible cause for movements in operating expenses as well. Finally, operating expenses are used as a possible explanatory variable for a property’s lease-up performance during the year.

The analysis consists of a series of regression models performed on data provided by the 2012 Rental Housing Finance Survey (RHFS) in the USA. The RHFS is a unique data set that covers a wide degree of information on multifamily properties. The RHFS represents 2,260 properties in total, and covers various aspects of the apartment industry, including financing and operational cost measures. Control variables used as independent variables include number of units, year of property acquisition, and age of building.

Individual ownership and self-management proved to be statistically significant drivers in driving down log operating expenses. Hours spent by individuals performing property management roles on their own properties had a slightly positive association with operating expenses. For professional managers, the fees devoted solely to the manager or management company had a highly significant and positive effect on other operating costs. Finally, when separating out the individual components of operating expenses, only two variables had significant effects on tenant lease-ups: management expenses (positive) and security expenses (negative).

The data set is potentially biased toward those properties with less than 100 units, and thus it would be problematic to assume that these findings are generalizable to the population at large. There are also no geographic coding indicators within the RHFS data set, which eliminates the potential to control for various market factors and rural/urban differences.

The research provides an understanding of some of the basic factors behind increases in operating expenses, which ultimately has implications for performance benchmarks such as net operating income and property market value.

The reasonable controlling of operating expenses ultimately has potentially positive implications for low- to moderate-income populations, who would ultimately experience lower rents as a result.

This research represents one of the first known uses of the RHFS database.

The purpose of this study is to examine the impact of crime on apartment prices for Hamburg, Germany, for the years 2012 to 2017.

The authors use a panel data setting with fixed effects estimators and temporal lags to moderate the endogeneity concerns related to crime. The authors consider the effect of total crime, violent and property crime and some sub-categories of crime.

The estimates show that it takes two to three years for prices to react, with the longer run elasticity reaching −0.12 for total crime, −0.15 for property crime and −0.06 for violent crime. The elasticities are much larger in high-crime areas (−0.22 for total crime, −0.28 and −0.09 for property and violent crime) and elevated also in low-income areas.

The finding that property crime matters more in terms of quantitative impact for housing values than violent crime provides reasonable grounds for rethinking the resource allocation of public spending on crime clearance and prevention in Germany. Far more emphasis on preventing property crime appears in order and especially so in the lower income or higher crime areas, which are significantly more affected by crime and in particular property crime than those in high income or low crime areas.

The estimates for Hamburg provide the first detailed results of the impact of crime on real estate prices in Germany. It is also the first study for Continental Europe using panel data.

Based on a hedonic regression approach, the purpose of this paper is to relativize existing green pay off evidence by incorporating Super Trophy as a so far underrepresented determinant. The authors analyze a private panel database of 160 European office properties and confirm a significant green pay off; the positive impact of excellent environmental certification results on market values and net rents is significantly reduced when considering Super Trophy characteristics.

Based on a panel database of 160 European office properties, the survey applies a hedonic regression approach including an extensive set of control factors as, for example, location criteria, general property characteristics, climate adjustments, consumption data, refurbishment activities, green leases, sustainable certification and energy performance certificate figures.

Even though our database still confirms a significant green pay off, the positive impact of excellent environmental certification results on market values and net rents is significantly reduced when controlling for Super Trophy characteristics.

Especially, the question how sustainability can be integrated into real estate appraisal is of major interest. The paper at hand may help in two aspects: on the one hand, it provides further insight with regard to the quantitative impact of Super Trophy Buildings on rents and market values. On the other hand, a higher transparency in appraisals may result in structural specifications that help to consolidate appraisals and empirical evidence on a “green pay off.”

The study investigates a niche segment – landmark properties. The empirical analysis explicitly controls for potential Super Trophy status. It draws attention to the importance of a reasonable and complete set of control variables to increase statistical validity of future studies in that field.

The purpose of this paper is to evaluate differences between the results of constant property and variable property approaches in solving the problem of Al2O3-water nanofluid heat transfer in an annular microchannel. Also, the effect of nanoparticle diameter on flow and heat transfer characteristics is investigated.

Thermo-physical properties of the nanofluid including density, specific heat, viscosity and thermal conductivity are assumed to be temperature dependent. Governing equations are descritized using the finite volume method and solved by SIMPLE algorithm.

The results reveal that the constant property assumption is unable to predict the correct trend of variations along the microchannel for some of the characteristics, especially when the range of temperature change near the wall is considerable. In the fully developed region, constant property solution overestimates the values of shear stress near the walls of the microchannel. In addition, the values of Nusselt numbers are different for the two solutions. Furthermore, a decrease in wall’s shear stress has been observed as a result of increasing nanoparticle size.

This paper reflects that how the friction factor and heat transfer vary along the microchannel in temperature dependent modeling, which is not reflected in the results of constant property approach. To the best of the authors’ knowledge, there is no similar investigation of the effect of nanofluid variable properties with Pr=5 or in annular geometry.

This paper examines the cyclical regularities of macroeconomic, financial and property market aggregates in relation to the property stock price cycle in the UK. The Hodrick Prescott filter is employed to fit a long‐term trend to the raw data, and to derive the short‐term cycles of each series. It is found that the cycles of consumer expenditure, total consumption per capita, the dividend yield and the long‐term bond yield are moderately correlated, and mainly coincident, with the property price cycle. There is also evidence that the nominal and real Treasury Bill rates and the interest rate spread lead this cycle by one or two quarters, and therefore that these series can be considered leading indicators of property stock prices. This study recommends that macroeconomic and financial variables can provide useful information to explain and potentially to forecast movements of property‐backed stock returns in the UK.

Applies the methodology adopted in contemporary business cycle research on establishing the stylized facts of aggregate output fluctuations, in the context of the office, industrial and retail building cycle. The objective of the study is to identify the degree to which cyclical regularities, which are in conformity with theoretical modelling, are identified across property sectors. Undertakes a statistical analysis of the cyclical properties of certain variables in relation to the building cycle in the respective commercial property sectors. The variables considered capture real economic conditions and trends in both the property and investment markets. The findings illustrate that certain variables display a cyclical pattern in relation to the property cycles which is in accordance with theoretical intuition. They also show that either other variables do not display any cyclical relationship to the commercial building cycles or the relationship does not conform to the predictions of the existing theoretical treatment of property development.

The retail property market is constantly adopting to the continuous demand of retailers and their consumers. This paper aims to investigate retail property market dynamics through spatial accessibility measures of the City of York street network. It explores how spatial accessibility metrics (SAM) explain retail market dynamics (RMD) through changes in the city’s retail rental values and stock.

Valuation office agency (VOA) data sets (aspatial) and ordnance survey map (spatial) data form the empirical foundation for this investigation. Changes in rental value and retail stock between 2010 and 2017 VOA data sets represent the RMD variables. While, the configured street network measures of Space Syntax, namely, global integration, local integration, global choice and normalised angular choice form the SAM variables. The relationship between these variables is analysed through geo-visualisation and statistical testing using GIS and SPSS tools.

The study reveals that there has been an overall negative changes of 15 and 22% in rental value and retail stock, respectively, even though some locations within the sampled city (York, North Yorkshire, England) indicated positive changes. The study further indicated that changes in retail rental value and stock have occurred within locations with good accessibility index. It also verifies that there are spatial and statistical relationship between variables and 22% of RMD variability was jointly accounted for by SAM.

This research is first to investigates changes in retail property market variables through spatial accessibility measures of space syntax. It contributes to the burgeoning research field of real estate and Space Syntax.

This study investigates the relation between residential property values and both property taxes and public services in Georgia’s counties. Capitalization theory predicts that property values relate negatively to property taxes, and positively to public services. Palmon and Smith (1998) state that errors in public service measures create a capitalization coefficient bias that makes it difficult to isolate tax effects from public service effects. This paper is a first step in defining and quantifying public services and their marginal effect on housing values. It develops public service measures in four quality-of-life areas – economy, education, health, and public safety. The models suggest a strong negative relation between effective tax rates and property values, and a significant positive association between the public service measures and property values. Analyses indicate that property taxes are capitalized into housing prices at greater than 100%, suggesting prior underestimations based on measurement errors in public service variables.